Overview

Brought to you by YData

Dataset statistics

Number of variables57
Number of observations15757
Missing cells12130
Missing cells (%)1.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory15.4 MiB
Average record size in memory1022.6 B

Variable types

Numeric8
Categorical41
Unsupported6
DateTime1
Text1

Alerts

Duracion en Terapia Intensiva is highly overall correlated with Duración de estadiaHigh correlation
Duración de estadia is highly overall correlated with Duracion en Terapia IntensivaHigh correlation
Edad is highly overall correlated with Edad (ubicaciones) and 1 other fieldsHigh correlation
Edad (ubicaciones) is highly overall correlated with Edad and 1 other fieldsHigh correlation
Enfermedad renal crónica is highly overall correlated with Lesión renal agudaHigh correlation
Grupo Etario is highly overall correlated with Edad and 1 other fieldsHigh correlation
ID is highly overall correlated with Nro clave admisionHigh correlation
Infarto de miocardio con elevación del ST is highly overall correlated with Síndrome coronario agudoHigh correlation
Insuficiencia cardíaca is highly overall correlated with Insuficiencia cardíaca con fracción de eyección normal and 1 other fieldsHigh correlation
Insuficiencia cardíaca con fracción de eyección normal is highly overall correlated with Insuficiencia cardíacaHigh correlation
Insuficiencia cardíaca con fracción de eyección reducida is highly overall correlated with Insuficiencia cardíacaHigh correlation
Lesión renal aguda is highly overall correlated with Enfermedad renal crónicaHigh correlation
Motivo Alta is highly overall correlated with SHOCKHigh correlation
Nro clave admision is highly overall correlated with IDHigh correlation
SHOCK is highly overall correlated with Motivo Alta and 1 other fieldsHigh correlation
Shock cardiogénico is highly overall correlated with SHOCKHigh correlation
Síndrome coronario agudo is highly overall correlated with Infarto de miocardio con elevación del STHigh correlation
Motivo Alta is highly imbalanced (57.4%) Imbalance
Fumador is highly imbalanced (71.2%) Imbalance
ALCOHOL is highly imbalanced (65.4%) Imbalance
Enfermedad renal crónica is highly imbalanced (53.6%) Imbalance
ANEMIA SEVERA is highly imbalanced (86.2%) Imbalance
ANGINA ESTABLE is highly imbalanced (59.1%) Imbalance
ATYPICAL CHEST PAIN is highly imbalanced (82.8%) Imbalance
Enfermedad valvular cardíaca is highly imbalanced (78.1%) Imbalance
Bloqueo auriculoventricular completo is highly imbalanced (82.5%) Imbalance
Síndrome del seno enfermo is highly imbalanced (94.1%) Imbalance
Accidente cerebrovascular isquémico is highly imbalanced (80.8%) Imbalance
Accidente cerebrovascular hemorrágico is highly imbalanced (96.0%) Imbalance
Fibrilación auricular is highly imbalanced (71.0%) Imbalance
Taquicardia ventricular is highly imbalanced (79.1%) Imbalance
Taquicardia supraventricular paroxística is highly imbalanced (93.6%) Imbalance
Enfermedad cardíaca congénita is highly imbalanced (91.6%) Imbalance
Infección del tracto urinario is highly imbalanced (66.5%) Imbalance
NEURO CARDIOGENIC SYNCOPE is highly imbalanced (93.0%) Imbalance
Hipotensión ortostática is highly imbalanced (93.4%) Imbalance
Endocarditis infecciosa is highly imbalanced (98.1%) Imbalance
Trombosis venosa profunda is highly imbalanced (89.8%) Imbalance
Shock cardiogénico is highly imbalanced (67.3%) Imbalance
SHOCK is highly imbalanced (72.8%) Imbalance
Embolia pulmonar is highly imbalanced (88.5%) Imbalance
Infección torácica is highly imbalanced (84.9%) Imbalance
Hemoglobina has 256 (1.6%) missing values Missing
Leucocitos has 290 (1.8%) missing values Missing
Plaquetas has 285 (1.8%) missing values Missing
GLUCOSA has 863 (5.5%) missing values Missing
UREA has 241 (1.5%) missing values Missing
Creatinina has 247 (1.6%) missing values Missing
Péptido natriurético tipo B has 8441 (53.6%) missing values Missing
Fracción de eyección has 1505 (9.6%) missing values Missing
Leucocitos is highly skewed (γ1 = 121.7945944) Skewed
Nro clave admision is uniformly distributed Uniform
Nro clave admision has unique values Unique
Plaquetas is an unsupported type, check if it needs cleaning or further analysis Unsupported
GLUCOSA is an unsupported type, check if it needs cleaning or further analysis Unsupported
UREA is an unsupported type, check if it needs cleaning or further analysis Unsupported
Creatinina is an unsupported type, check if it needs cleaning or further analysis Unsupported
Péptido natriurético tipo B is an unsupported type, check if it needs cleaning or further analysis Unsupported
Fracción de eyección is an unsupported type, check if it needs cleaning or further analysis Unsupported
Duracion en Terapia Intensiva has 2761 (17.5%) zeros Zeros

Reproduction

Analysis started2025-01-17 08:47:19.943707
Analysis finished2025-01-17 08:47:43.875256
Duration23.93 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

Nro clave admision
Real number (ℝ)

High correlation  Uniform  Unique 

Distinct15757
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7879
Minimum1
Maximum15757
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size123.2 KiB
2025-01-17T08:47:44.012238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile788.8
Q13940
median7879
Q311818
95-th percentile14969.2
Maximum15757
Range15756
Interquartile range (IQR)7878

Descriptive statistics

Standard deviation4548.7984
Coefficient of variation (CV)0.57733195
Kurtosis-1.2
Mean7879
Median Absolute Deviation (MAD)3939
Skewness0
Sum1.241494 × 108
Variance20691567
MonotonicityNot monotonic
2025-01-17T08:47:44.231818image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8656 1
 
< 0.1%
1357 1
 
< 0.1%
9559 1
 
< 0.1%
1840 1
 
< 0.1%
2511 1
 
< 0.1%
6132 1
 
< 0.1%
2320 1
 
< 0.1%
2341 1
 
< 0.1%
2362 1
 
< 0.1%
9926 1
 
< 0.1%
Other values (15747) 15747
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
15757 1
< 0.1%
15756 1
< 0.1%
15755 1
< 0.1%
15754 1
< 0.1%
15753 1
< 0.1%
15752 1
< 0.1%
15751 1
< 0.1%
15750 1
< 0.1%
15749 1
< 0.1%
15748 1
< 0.1%

ID
Real number (ℝ)

High correlation 

Distinct12243
Distinct (%)77.7%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean411749.35
Minimum506
Maximum6408503
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size123.2 KiB
2025-01-17T08:47:44.425703image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum506
5-th percentile127725
Q1278580.75
median406701
Q3564337.5
95-th percentile671720
Maximum6408503
Range6407997
Interquartile range (IQR)285756.75

Descriptive statistics

Standard deviation199221.63
Coefficient of variation (CV)0.483842
Kurtosis142.72199
Mean411749.35
Median Absolute Deviation (MAD)139834
Skewness5.5365486
Sum6.4875228 × 109
Variance3.9689258 × 1010
MonotonicityNot monotonic
2025-01-17T08:47:44.613725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
256628 17
 
0.1%
273016 15
 
0.1%
325715 11
 
0.1%
177926 11
 
0.1%
156041 9
 
0.1%
351289 9
 
0.1%
153716 9
 
0.1%
211418 9
 
0.1%
415865 8
 
0.1%
201243 8
 
0.1%
Other values (12233) 15650
99.3%
ValueCountFrequency (%)
506 1
 
< 0.1%
798 3
< 0.1%
989 3
< 0.1%
1006 1
 
< 0.1%
1060 1
 
< 0.1%
1196 1
 
< 0.1%
1261 1
 
< 0.1%
1843 1
 
< 0.1%
3180 1
 
< 0.1%
4499 1
 
< 0.1%
ValueCountFrequency (%)
6408503 1
< 0.1%
5711587 1
< 0.1%
4888926 1
< 0.1%
4888286 1
< 0.1%
4888078 1
< 0.1%
4562014 1
< 0.1%
987456 1
< 0.1%
957621 1
< 0.1%
873967 1
< 0.1%
828660 2
< 0.1%

Edad
Real number (ℝ)

High correlation 

Distinct96
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61.42616
Minimum4
Maximum110
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size123.2 KiB
2025-01-17T08:47:44.793393image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile38
Q154
median62
Q370
95-th percentile82
Maximum110
Range106
Interquartile range (IQR)16

Descriptive statistics

Standard deviation13.420862
Coefficient of variation (CV)0.21848772
Kurtosis0.63769085
Mean61.42616
Median Absolute Deviation (MAD)8
Skewness-0.50405016
Sum967892
Variance180.11953
MonotonicityNot monotonic
2025-01-17T08:47:44.990910image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
65 912
 
5.8%
60 884
 
5.6%
70 769
 
4.9%
55 640
 
4.1%
62 603
 
3.8%
75 541
 
3.4%
58 471
 
3.0%
50 440
 
2.8%
72 429
 
2.7%
63 427
 
2.7%
Other values (86) 9641
61.2%
ValueCountFrequency (%)
4 8
0.1%
5 1
 
< 0.1%
6 2
 
< 0.1%
7 4
< 0.1%
9 1
 
< 0.1%
10 4
< 0.1%
11 4
< 0.1%
12 3
 
< 0.1%
13 5
< 0.1%
14 2
 
< 0.1%
ValueCountFrequency (%)
110 2
 
< 0.1%
99 5
 
< 0.1%
98 2
 
< 0.1%
97 4
 
< 0.1%
96 4
 
< 0.1%
95 11
0.1%
94 9
0.1%
93 1
 
< 0.1%
92 14
0.1%
91 8
0.1%

Género
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1010.1 KiB
Masculino
9990 
Femenino
5767 

Length

Max length9
Median length9
Mean length8.6340039
Min length8

Characters and Unicode

Total characters136046
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemenino
2nd rowMasculino
3rd rowMasculino
4th rowMasculino
5th rowMasculino

Common Values

ValueCountFrequency (%)
Masculino 9990
63.4%
Femenino 5767
36.6%

Length

2025-01-17T08:47:45.149431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-17T08:47:45.234236image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
masculino 9990
63.4%
femenino 5767
36.6%

Most occurring characters

ValueCountFrequency (%)
n 21524
15.8%
i 15757
11.6%
o 15757
11.6%
e 11534
8.5%
M 9990
7.3%
a 9990
7.3%
s 9990
7.3%
c 9990
7.3%
u 9990
7.3%
l 9990
7.3%
Other values (2) 11534
8.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 136046
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 21524
15.8%
i 15757
11.6%
o 15757
11.6%
e 11534
8.5%
M 9990
7.3%
a 9990
7.3%
s 9990
7.3%
c 9990
7.3%
u 9990
7.3%
l 9990
7.3%
Other values (2) 11534
8.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 136046
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 21524
15.8%
i 15757
11.6%
o 15757
11.6%
e 11534
8.5%
M 9990
7.3%
a 9990
7.3%
s 9990
7.3%
c 9990
7.3%
u 9990
7.3%
l 9990
7.3%
Other values (2) 11534
8.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 136046
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 21524
15.8%
i 15757
11.6%
o 15757
11.6%
e 11534
8.5%
M 9990
7.3%
a 9990
7.3%
s 9990
7.3%
c 9990
7.3%
u 9990
7.3%
l 9990
7.3%
Other values (2) 11534
8.5%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size966.0 KiB
Urbana
12077 
Rural
3680 

Length

Max length6
Median length6
Mean length5.766453
Min length5

Characters and Unicode

Total characters90862
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRural
2nd rowUrbana
3rd rowRural
4th rowRural
5th rowUrbana

Common Values

ValueCountFrequency (%)
Urbana 12077
76.6%
Rural 3680
 
23.4%

Length

2025-01-17T08:47:45.345347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-17T08:47:45.430213image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
urbana 12077
76.6%
rural 3680
 
23.4%

Most occurring characters

ValueCountFrequency (%)
a 27834
30.6%
r 15757
17.3%
U 12077
13.3%
b 12077
13.3%
n 12077
13.3%
R 3680
 
4.1%
u 3680
 
4.1%
l 3680
 
4.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 90862
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 27834
30.6%
r 15757
17.3%
U 12077
13.3%
b 12077
13.3%
n 12077
13.3%
R 3680
 
4.1%
u 3680
 
4.1%
l 3680
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 90862
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 27834
30.6%
r 15757
17.3%
U 12077
13.3%
b 12077
13.3%
n 12077
13.3%
R 3680
 
4.1%
u 3680
 
4.1%
l 3680
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 90862
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 27834
30.6%
r 15757
17.3%
U 12077
13.3%
b 12077
13.3%
n 12077
13.3%
R 3680
 
4.1%
u 3680
 
4.1%
l 3680
 
4.1%

Tipo de Admision
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
Emergencia
10924 
Ambulatoria
4833 

Length

Max length11
Median length10
Mean length10.306721
Min length10

Characters and Unicode

Total characters162403
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEmergencia
2nd rowEmergencia
3rd rowEmergencia
4th rowAmbulatoria
5th rowEmergencia

Common Values

ValueCountFrequency (%)
Emergencia 10924
69.3%
Ambulatoria 4833
30.7%

Length

2025-01-17T08:47:45.541129image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-17T08:47:45.627335image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
emergencia 10924
69.3%
ambulatoria 4833
30.7%

Most occurring characters

ValueCountFrequency (%)
e 21848
13.5%
a 20590
12.7%
m 15757
9.7%
r 15757
9.7%
i 15757
9.7%
E 10924
6.7%
g 10924
6.7%
n 10924
6.7%
c 10924
6.7%
A 4833
 
3.0%
Other values (5) 24165
14.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 162403
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 21848
13.5%
a 20590
12.7%
m 15757
9.7%
r 15757
9.7%
i 15757
9.7%
E 10924
6.7%
g 10924
6.7%
n 10924
6.7%
c 10924
6.7%
A 4833
 
3.0%
Other values (5) 24165
14.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 162403
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 21848
13.5%
a 20590
12.7%
m 15757
9.7%
r 15757
9.7%
i 15757
9.7%
E 10924
6.7%
g 10924
6.7%
n 10924
6.7%
c 10924
6.7%
A 4833
 
3.0%
Other values (5) 24165
14.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 162403
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 21848
13.5%
a 20590
12.7%
m 15757
9.7%
r 15757
9.7%
i 15757
9.7%
E 10924
6.7%
g 10924
6.7%
n 10924
6.7%
c 10924
6.7%
A 4833
 
3.0%
Other values (5) 24165
14.9%

Duración de estadia
Real number (ℝ)

High correlation 

Distinct53
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4150536
Minimum1
Maximum98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size123.2 KiB
2025-01-17T08:47:45.795997image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median5
Q38
95-th percentile15
Maximum98
Range97
Interquartile range (IQR)5

Descriptive statistics

Standard deviation5.011421
Coefficient of variation (CV)0.78119706
Kurtosis20.720327
Mean6.4150536
Median Absolute Deviation (MAD)2
Skewness3.1084204
Sum101082
Variance25.114341
MonotonicityNot monotonic
2025-01-17T08:47:46.013885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 2109
13.4%
5 1904
12.1%
3 1878
11.9%
4 1836
11.7%
6 1623
10.3%
7 1294
8.2%
8 1069
6.8%
9 732
 
4.6%
1 582
 
3.7%
10 579
 
3.7%
Other values (43) 2151
13.7%
ValueCountFrequency (%)
1 582
 
3.7%
2 2109
13.4%
3 1878
11.9%
4 1836
11.7%
5 1904
12.1%
6 1623
10.3%
7 1294
8.2%
8 1069
6.8%
9 732
 
4.6%
10 579
 
3.7%
ValueCountFrequency (%)
98 1
 
< 0.1%
67 1
 
< 0.1%
58 1
 
< 0.1%
53 1
 
< 0.1%
52 4
< 0.1%
50 2
< 0.1%
49 1
 
< 0.1%
48 3
< 0.1%
47 2
< 0.1%
46 2
< 0.1%

Duracion en Terapia Intensiva
Real number (ℝ)

High correlation  Zeros 

Distinct45
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8037698
Minimum0
Maximum58
Zeros2761
Zeros (%)17.5%
Negative0
Negative (%)0.0%
Memory size123.2 KiB
2025-01-17T08:47:46.187366image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile11
Maximum58
Range58
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.0156401
Coefficient of variation (CV)1.0557001
Kurtosis15.454725
Mean3.8037698
Median Absolute Deviation (MAD)2
Skewness2.8481612
Sum59936
Variance16.125365
MonotonicityNot monotonic
2025-01-17T08:47:46.354573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
0 2761
17.5%
2 2620
16.6%
3 1928
12.2%
4 1762
11.2%
1 1707
10.8%
5 1532
9.7%
6 918
 
5.8%
7 637
 
4.0%
8 476
 
3.0%
9 341
 
2.2%
Other values (35) 1075
 
6.8%
ValueCountFrequency (%)
0 2761
17.5%
1 1707
10.8%
2 2620
16.6%
3 1928
12.2%
4 1762
11.2%
5 1532
9.7%
6 918
 
5.8%
7 637
 
4.0%
8 476
 
3.0%
9 341
 
2.2%
ValueCountFrequency (%)
58 1
< 0.1%
48 2
< 0.1%
45 1
< 0.1%
42 1
< 0.1%
41 1
< 0.1%
40 2
< 0.1%
39 1
< 0.1%
38 1
< 0.1%
37 2
< 0.1%
36 2
< 0.1%

Motivo Alta
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1008.1 KiB
DISCHARGE
13756 
EXPIRY
 
1105
DAMA
 
896

Length

Max length9
Median length9
Mean length8.5052992
Min length4

Characters and Unicode

Total characters134018
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDAMA
2nd rowDAMA
3rd rowDAMA
4th rowDAMA
5th rowDAMA

Common Values

ValueCountFrequency (%)
DISCHARGE 13756
87.3%
EXPIRY 1105
 
7.0%
DAMA 896
 
5.7%

Length

2025-01-17T08:47:46.518894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-17T08:47:46.621265image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
discharge 13756
87.3%
expiry 1105
 
7.0%
dama 896
 
5.7%

Most occurring characters

ValueCountFrequency (%)
A 15548
11.6%
I 14861
11.1%
R 14861
11.1%
E 14861
11.1%
D 14652
10.9%
S 13756
10.3%
C 13756
10.3%
H 13756
10.3%
G 13756
10.3%
X 1105
 
0.8%
Other values (3) 3106
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 134018
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 15548
11.6%
I 14861
11.1%
R 14861
11.1%
E 14861
11.1%
D 14652
10.9%
S 13756
10.3%
C 13756
10.3%
H 13756
10.3%
G 13756
10.3%
X 1105
 
0.8%
Other values (3) 3106
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 134018
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 15548
11.6%
I 14861
11.1%
R 14861
11.1%
E 14861
11.1%
D 14652
10.9%
S 13756
10.3%
C 13756
10.3%
H 13756
10.3%
G 13756
10.3%
X 1105
 
0.8%
Other values (3) 3106
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 134018
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 15548
11.6%
I 14861
11.1%
R 14861
11.1%
E 14861
11.1%
D 14652
10.9%
S 13756
10.3%
C 13756
10.3%
H 13756
10.3%
G 13756
10.3%
X 1105
 
0.8%
Other values (3) 3106
 
2.3%

Fumador
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size892.6 KiB
0
14964 
1
 
793

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15757
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14964
95.0%
1 793
 
5.0%

Length

2025-01-17T08:47:46.731809image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-17T08:47:46.817040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 14964
95.0%
1 793
 
5.0%

Most occurring characters

ValueCountFrequency (%)
0 14964
95.0%
1 793
 
5.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 14964
95.0%
1 793
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 14964
95.0%
1 793
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 14964
95.0%
1 793
 
5.0%

ALCOHOL
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size892.6 KiB
0
14736 
1
 
1021

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15757
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14736
93.5%
1 1021
 
6.5%

Length

2025-01-17T08:47:46.926419image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-17T08:47:47.037947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 14736
93.5%
1 1021
 
6.5%

Most occurring characters

ValueCountFrequency (%)
0 14736
93.5%
1 1021
 
6.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 14736
93.5%
1 1021
 
6.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 14736
93.5%
1 1021
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 14736
93.5%
1 1021
 
6.5%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size892.6 KiB
0
10660 
1
5097 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15757
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 10660
67.7%
1 5097
32.3%

Length

2025-01-17T08:47:47.241959image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-17T08:47:47.344917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 10660
67.7%
1 5097
32.3%

Most occurring characters

ValueCountFrequency (%)
0 10660
67.7%
1 5097
32.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 10660
67.7%
1 5097
32.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 10660
67.7%
1 5097
32.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 10660
67.7%
1 5097
32.3%

Hipertensión
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size892.6 KiB
0
8101 
1
7656 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15757
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 8101
51.4%
1 7656
48.6%

Length

2025-01-17T08:47:47.501614image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-17T08:47:48.036875image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 8101
51.4%
1 7656
48.6%

Most occurring characters

ValueCountFrequency (%)
0 8101
51.4%
1 7656
48.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 8101
51.4%
1 7656
48.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 8101
51.4%
1 7656
48.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 8101
51.4%
1 7656
48.6%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size892.6 KiB
1
10551 
0
5206 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15757
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 10551
67.0%
0 5206
33.0%

Length

2025-01-17T08:47:48.253278image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-17T08:47:48.428371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 10551
67.0%
0 5206
33.0%

Most occurring characters

ValueCountFrequency (%)
1 10551
67.0%
0 5206
33.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 10551
67.0%
0 5206
33.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 10551
67.0%
0 5206
33.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 10551
67.0%
0 5206
33.0%

Miocardiopatía
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size892.6 KiB
0
13323 
1
2434 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15757
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 13323
84.6%
1 2434
 
15.4%

Length

2025-01-17T08:47:48.653628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-17T08:47:48.847757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 13323
84.6%
1 2434
 
15.4%

Most occurring characters

ValueCountFrequency (%)
0 13323
84.6%
1 2434
 
15.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 13323
84.6%
1 2434
 
15.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 13323
84.6%
1 2434
 
15.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 13323
84.6%
1 2434
 
15.4%

Enfermedad renal crónica
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size892.6 KiB
0
14207 
1
1550 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15757
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14207
90.2%
1 1550
 
9.8%

Length

2025-01-17T08:47:49.110169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-17T08:47:49.275244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 14207
90.2%
1 1550
 
9.8%

Most occurring characters

ValueCountFrequency (%)
0 14207
90.2%
1 1550
 
9.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 14207
90.2%
1 1550
 
9.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 14207
90.2%
1 1550
 
9.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 14207
90.2%
1 1550
 
9.8%

Hemoglobina
Real number (ℝ)

Missing 

Distinct181
Distinct (%)1.2%
Missing256
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean112.81595
Minimum3
Maximum1508
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size123.2 KiB
2025-01-17T08:47:49.523197image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile13
Q199
median121
Q3137
95-th percentile156
Maximum1508
Range1505
Interquartile range (IQR)38

Descriptive statistics

Standard deviation48.577247
Coefficient of variation (CV)0.43058847
Kurtosis162.68292
Mean112.81595
Median Absolute Deviation (MAD)18
Skewness6.7397148
Sum1748760
Variance2359.7489
MonotonicityNot monotonic
2025-01-17T08:47:49.932961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126 300
 
1.9%
124 293
 
1.9%
129 292
 
1.9%
133 289
 
1.8%
136 287
 
1.8%
121 285
 
1.8%
131 282
 
1.8%
13 276
 
1.8%
139 274
 
1.7%
119 270
 
1.7%
Other values (171) 12653
80.3%
ValueCountFrequency (%)
3 1
 
< 0.1%
4 1
 
< 0.1%
5 7
 
< 0.1%
6 10
 
0.1%
7 26
 
0.2%
8 48
 
0.3%
9 106
0.7%
10 150
1.0%
11 173
1.1%
12 233
1.5%
ValueCountFrequency (%)
1508 1
< 0.1%
1265 1
< 0.1%
1208 1
< 0.1%
1189 1
< 0.1%
1106 1
< 0.1%
1101 1
< 0.1%
1069 1
< 0.1%
1004 2
< 0.1%
998 2
< 0.1%
837 1
< 0.1%

Leucocitos
Real number (ℝ)

Missing  Skewed 

Distinct398
Distinct (%)2.6%
Missing290
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean109.85615
Minimum1
Maximum79278
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size123.2 KiB
2025-01-17T08:47:50.304850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10
Q171
median96
Q3128
95-th percentile211
Maximum79278
Range79277
Interquartile range (IQR)57

Descriptive statistics

Standard deviation641.08164
Coefficient of variation (CV)5.8356466
Kurtosis15040.179
Mean109.85615
Median Absolute Deviation (MAD)28
Skewness121.79459
Sum1699145
Variance410985.67
MonotonicityNot monotonic
2025-01-17T08:47:50.594240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
84 209
 
1.3%
89 208
 
1.3%
79 207
 
1.3%
86 203
 
1.3%
94 202
 
1.3%
78 199
 
1.3%
91 198
 
1.3%
8 195
 
1.2%
81 194
 
1.2%
96 189
 
1.2%
Other values (388) 13463
85.4%
(Missing) 290
 
1.8%
ValueCountFrequency (%)
1 4
 
< 0.1%
2 3
 
< 0.1%
3 9
 
0.1%
4 28
 
0.2%
5 33
 
0.2%
6 90
0.6%
7 149
0.9%
8 195
1.2%
9 164
1.0%
10 165
1.0%
ValueCountFrequency (%)
79278 1
< 0.1%
2118 2
< 0.1%
1703 1
< 0.1%
1407 1
< 0.1%
1347 1
< 0.1%
1258 1
< 0.1%
1239 1
< 0.1%
1121 1
< 0.1%
1102 1
< 0.1%
988 1
< 0.1%

Plaquetas
Unsupported

Missing  Rejected  Unsupported 

Missing285
Missing (%)1.8%
Memory size553.6 KiB

GLUCOSA
Unsupported

Missing  Rejected  Unsupported 

Missing863
Missing (%)5.5%
Memory size552.9 KiB

UREA
Unsupported

Missing  Rejected  Unsupported 

Missing241
Missing (%)1.5%
Memory size554.0 KiB

Creatinina
Unsupported

Missing  Rejected  Unsupported 

Missing247
Missing (%)1.6%
Memory size892.8 KiB

Péptido natriurético tipo B
Unsupported

Missing  Rejected  Unsupported 

Missing8441
Missing (%)53.6%
Memory size537.8 KiB
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size892.6 KiB
0
12635 
1
3122 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15757
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 12635
80.2%
1 3122
 
19.8%

Length

2025-01-17T08:47:50.761971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-17T08:47:50.850371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 12635
80.2%
1 3122
 
19.8%

Most occurring characters

ValueCountFrequency (%)
0 12635
80.2%
1 3122
 
19.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 12635
80.2%
1 3122
 
19.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 12635
80.2%
1 3122
 
19.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 12635
80.2%
1 3122
 
19.8%

Fracción de eyección
Unsupported

Missing  Rejected  Unsupported 

Missing1505
Missing (%)9.6%
Memory size550.6 KiB

ANEMIA SEVERA
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size892.6 KiB
0
15452 
1
 
305

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15757
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15452
98.1%
1 305
 
1.9%

Length

2025-01-17T08:47:50.977074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-17T08:47:51.070683image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 15452
98.1%
1 305
 
1.9%

Most occurring characters

ValueCountFrequency (%)
0 15452
98.1%
1 305
 
1.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 15452
98.1%
1 305
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 15452
98.1%
1 305
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 15452
98.1%
1 305
 
1.9%

ANEMIA
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size892.6 KiB
0
12970 
1
2787 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15757
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 12970
82.3%
1 2787
 
17.7%

Length

2025-01-17T08:47:51.173487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-17T08:47:51.259825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 12970
82.3%
1 2787
 
17.7%

Most occurring characters

ValueCountFrequency (%)
0 12970
82.3%
1 2787
 
17.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 12970
82.3%
1 2787
 
17.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 12970
82.3%
1 2787
 
17.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 12970
82.3%
1 2787
 
17.7%

ANGINA ESTABLE
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size892.6 KiB
0
14468 
1
 
1289

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15757
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14468
91.8%
1 1289
 
8.2%

Length

2025-01-17T08:47:51.367662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-17T08:47:51.449625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 14468
91.8%
1 1289
 
8.2%

Most occurring characters

ValueCountFrequency (%)
0 14468
91.8%
1 1289
 
8.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 14468
91.8%
1 1289
 
8.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 14468
91.8%
1 1289
 
8.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 14468
91.8%
1 1289
 
8.2%

Síndrome coronario agudo
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size892.6 KiB
0
9994 
1
5763 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15757
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 9994
63.4%
1 5763
36.6%

Length

2025-01-17T08:47:51.558409image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-17T08:47:51.645438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 9994
63.4%
1 5763
36.6%

Most occurring characters

ValueCountFrequency (%)
0 9994
63.4%
1 5763
36.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 9994
63.4%
1 5763
36.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 9994
63.4%
1 5763
36.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 9994
63.4%
1 5763
36.6%

Infarto de miocardio con elevación del ST
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size892.6 KiB
0
13555 
1
2202 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15757
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13555
86.0%
1 2202
 
14.0%

Length

2025-01-17T08:47:51.759215image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-17T08:47:51.847620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 13555
86.0%
1 2202
 
14.0%

Most occurring characters

ValueCountFrequency (%)
0 13555
86.0%
1 2202
 
14.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 13555
86.0%
1 2202
 
14.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 13555
86.0%
1 2202
 
14.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 13555
86.0%
1 2202
 
14.0%

ATYPICAL CHEST PAIN
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size892.6 KiB
0
15354 
1
 
403

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15757
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15354
97.4%
1 403
 
2.6%

Length

2025-01-17T08:47:51.956306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-17T08:47:52.043070image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 15354
97.4%
1 403
 
2.6%

Most occurring characters

ValueCountFrequency (%)
0 15354
97.4%
1 403
 
2.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 15354
97.4%
1 403
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 15354
97.4%
1 403
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 15354
97.4%
1 403
 
2.6%

Insuficiencia cardíaca
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size892.6 KiB
0
11196 
1
4561 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15757
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 11196
71.1%
1 4561
28.9%

Length

2025-01-17T08:47:52.143514image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-17T08:47:52.223327image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 11196
71.1%
1 4561
28.9%

Most occurring characters

ValueCountFrequency (%)
0 11196
71.1%
1 4561
28.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 11196
71.1%
1 4561
28.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 11196
71.1%
1 4561
28.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 11196
71.1%
1 4561
28.9%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size892.6 KiB
0
13336 
1
2421 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15757
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 13336
84.6%
1 2421
 
15.4%

Length

2025-01-17T08:47:52.344863image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-17T08:47:52.441835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 13336
84.6%
1 2421
 
15.4%

Most occurring characters

ValueCountFrequency (%)
0 13336
84.6%
1 2421
 
15.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 13336
84.6%
1 2421
 
15.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 13336
84.6%
1 2421
 
15.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 13336
84.6%
1 2421
 
15.4%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size892.6 KiB
0
13605 
1
2152 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15757
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13605
86.3%
1 2152
 
13.7%

Length

2025-01-17T08:47:52.571080image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-17T08:47:52.671559image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 13605
86.3%
1 2152
 
13.7%

Most occurring characters

ValueCountFrequency (%)
0 13605
86.3%
1 2152
 
13.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 13605
86.3%
1 2152
 
13.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 13605
86.3%
1 2152
 
13.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 13605
86.3%
1 2152
 
13.7%

Enfermedad valvular cardíaca
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size892.6 KiB
0
15205 
1
 
552

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15757
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15205
96.5%
1 552
 
3.5%

Length

2025-01-17T08:47:52.795257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-17T08:47:52.880015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 15205
96.5%
1 552
 
3.5%

Most occurring characters

ValueCountFrequency (%)
0 15205
96.5%
1 552
 
3.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 15205
96.5%
1 552
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 15205
96.5%
1 552
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 15205
96.5%
1 552
 
3.5%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size892.6 KiB
0
15345 
1
 
412

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15757
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15345
97.4%
1 412
 
2.6%

Length

2025-01-17T08:47:52.991917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-17T08:47:53.081712image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 15345
97.4%
1 412
 
2.6%

Most occurring characters

ValueCountFrequency (%)
0 15345
97.4%
1 412
 
2.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 15345
97.4%
1 412
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 15345
97.4%
1 412
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 15345
97.4%
1 412
 
2.6%

Síndrome del seno enfermo
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size892.6 KiB
0
15650 
1
 
107

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15757
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15650
99.3%
1 107
 
0.7%

Length

2025-01-17T08:47:53.188464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-17T08:47:53.269085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 15650
99.3%
1 107
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 15650
99.3%
1 107
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 15650
99.3%
1 107
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 15650
99.3%
1 107
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 15650
99.3%
1 107
 
0.7%

Lesión renal aguda
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size892.6 KiB
0
12253 
1
3504 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15757
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 12253
77.8%
1 3504
 
22.2%

Length

2025-01-17T08:47:53.391836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-17T08:47:53.485717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 12253
77.8%
1 3504
 
22.2%

Most occurring characters

ValueCountFrequency (%)
0 12253
77.8%
1 3504
 
22.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 12253
77.8%
1 3504
 
22.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 12253
77.8%
1 3504
 
22.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 12253
77.8%
1 3504
 
22.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size892.6 KiB
0
15293 
1
 
464

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15757
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15293
97.1%
1 464
 
2.9%

Length

2025-01-17T08:47:53.589298image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-17T08:47:53.670621image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 15293
97.1%
1 464
 
2.9%

Most occurring characters

ValueCountFrequency (%)
0 15293
97.1%
1 464
 
2.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 15293
97.1%
1 464
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 15293
97.1%
1 464
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 15293
97.1%
1 464
 
2.9%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size892.6 KiB
0
15690 
1
 
67

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15757
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15690
99.6%
1 67
 
0.4%

Length

2025-01-17T08:47:53.770074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-17T08:47:53.852657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 15690
99.6%
1 67
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 15690
99.6%
1 67
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 15690
99.6%
1 67
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 15690
99.6%
1 67
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 15690
99.6%
1 67
 
0.4%

Fibrilación auricular
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size892.6 KiB
0
14957 
1
 
800

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15757
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14957
94.9%
1 800
 
5.1%

Length

2025-01-17T08:47:53.963163image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-17T08:47:54.057835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 14957
94.9%
1 800
 
5.1%

Most occurring characters

ValueCountFrequency (%)
0 14957
94.9%
1 800
 
5.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 14957
94.9%
1 800
 
5.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 14957
94.9%
1 800
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 14957
94.9%
1 800
 
5.1%

Taquicardia ventricular
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size892.6 KiB
0
15238 
1
 
519

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15757
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15238
96.7%
1 519
 
3.3%

Length

2025-01-17T08:47:54.162448image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-17T08:47:54.244719image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 15238
96.7%
1 519
 
3.3%

Most occurring characters

ValueCountFrequency (%)
0 15238
96.7%
1 519
 
3.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 15238
96.7%
1 519
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 15238
96.7%
1 519
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 15238
96.7%
1 519
 
3.3%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size892.6 KiB
0
15638 
1
 
119

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15757
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15638
99.2%
1 119
 
0.8%

Length

2025-01-17T08:47:54.346749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-17T08:47:54.437451image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 15638
99.2%
1 119
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 15638
99.2%
1 119
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 15638
99.2%
1 119
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 15638
99.2%
1 119
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 15638
99.2%
1 119
 
0.8%

Enfermedad cardíaca congénita
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size892.6 KiB
0
15592 
1
 
165

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15757
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15592
99.0%
1 165
 
1.0%

Length

2025-01-17T08:47:54.541288image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-17T08:47:54.624287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 15592
99.0%
1 165
 
1.0%

Most occurring characters

ValueCountFrequency (%)
0 15592
99.0%
1 165
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 15592
99.0%
1 165
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 15592
99.0%
1 165
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 15592
99.0%
1 165
 
1.0%

Infección del tracto urinario
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size892.6 KiB
0
14782 
1
 
975

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15757
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14782
93.8%
1 975
 
6.2%

Length

2025-01-17T08:47:54.730361image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-17T08:47:54.815797image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 14782
93.8%
1 975
 
6.2%

Most occurring characters

ValueCountFrequency (%)
0 14782
93.8%
1 975
 
6.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 14782
93.8%
1 975
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 14782
93.8%
1 975
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 14782
93.8%
1 975
 
6.2%

NEURO CARDIOGENIC SYNCOPE
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size892.6 KiB
0
15625 
1
 
132

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15757
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15625
99.2%
1 132
 
0.8%

Length

2025-01-17T08:47:54.921930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-17T08:47:55.003121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 15625
99.2%
1 132
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 15625
99.2%
1 132
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 15625
99.2%
1 132
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 15625
99.2%
1 132
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 15625
99.2%
1 132
 
0.8%

Hipotensión ortostática
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size892.6 KiB
0
15633 
1
 
124

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15757
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15633
99.2%
1 124
 
0.8%

Length

2025-01-17T08:47:55.103508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-17T08:47:55.182929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 15633
99.2%
1 124
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 15633
99.2%
1 124
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 15633
99.2%
1 124
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 15633
99.2%
1 124
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 15633
99.2%
1 124
 
0.8%

Endocarditis infecciosa
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size892.6 KiB
0
15728 
1
 
29

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15757
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15728
99.8%
1 29
 
0.2%

Length

2025-01-17T08:47:55.283050image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-17T08:47:55.362824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 15728
99.8%
1 29
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 15728
99.8%
1 29
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 15728
99.8%
1 29
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 15728
99.8%
1 29
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 15728
99.8%
1 29
 
0.2%

Trombosis venosa profunda
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size892.6 KiB
0
15548 
1
 
209

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15757
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15548
98.7%
1 209
 
1.3%

Length

2025-01-17T08:47:55.482915image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-17T08:47:55.940313image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 15548
98.7%
1 209
 
1.3%

Most occurring characters

ValueCountFrequency (%)
0 15548
98.7%
1 209
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 15548
98.7%
1 209
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 15548
98.7%
1 209
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 15548
98.7%
1 209
 
1.3%

Shock cardiogénico
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size892.6 KiB
0
14813 
1
 
944

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15757
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14813
94.0%
1 944
 
6.0%

Length

2025-01-17T08:47:56.055969image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-17T08:47:56.149529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 14813
94.0%
1 944
 
6.0%

Most occurring characters

ValueCountFrequency (%)
0 14813
94.0%
1 944
 
6.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 14813
94.0%
1 944
 
6.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 14813
94.0%
1 944
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 14813
94.0%
1 944
 
6.0%

SHOCK
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size892.6 KiB
0
15022 
1
 
735

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15757
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15022
95.3%
1 735
 
4.7%

Length

2025-01-17T08:47:56.264578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-17T08:47:56.358325image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 15022
95.3%
1 735
 
4.7%

Most occurring characters

ValueCountFrequency (%)
0 15022
95.3%
1 735
 
4.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 15022
95.3%
1 735
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 15022
95.3%
1 735
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 15022
95.3%
1 735
 
4.7%

Embolia pulmonar
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size892.6 KiB
0
15515 
1
 
242

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15757
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15515
98.5%
1 242
 
1.5%

Length

2025-01-17T08:47:56.493110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-17T08:47:56.591871image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 15515
98.5%
1 242
 
1.5%

Most occurring characters

ValueCountFrequency (%)
0 15515
98.5%
1 242
 
1.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 15515
98.5%
1 242
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 15515
98.5%
1 242
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15757
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 15515
98.5%
1 242
 
1.5%

Infección torácica
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size923.4 KiB
0.0
15415 
1.0
 
341

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters47268
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 15415
97.8%
1.0 341
 
2.2%
(Missing) 1
 
< 0.1%

Length

2025-01-17T08:47:56.705746image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-17T08:47:56.806549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 15415
97.8%
1.0 341
 
2.2%

Most occurring characters

ValueCountFrequency (%)
0 31171
65.9%
. 15756
33.3%
1 341
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 47268
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 31171
65.9%
. 15756
33.3%
1 341
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 47268
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 31171
65.9%
. 15756
33.3%
1 341
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 47268
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 31171
65.9%
. 15756
33.3%
1 341
 
0.7%
Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size123.2 KiB
Minimum2017-04-01 00:00:00
Maximum2019-03-01 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-01-17T08:47:56.904310image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-17T08:47:57.050762image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
Distinct739
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2025-01-17T08:47:57.286309image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length35
Median length32
Mean length28.774703
Min length24

Characters and Unicode

Total characters453403
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowmiércoles, 30 de mayo de 2018
2nd rowviernes, 7 de diciembre de 2018
3rd rowsábado, 25 de noviembre de 2017
4th rowviernes, 24 de noviembre de 2017
5th rowmiércoles, 8 de noviembre de 2017
ValueCountFrequency (%)
de 31514
33.3%
2018 7816
 
8.3%
2017 5416
 
5.7%
2019 2525
 
2.7%
miércoles 2502
 
2.6%
viernes 2377
 
2.5%
jueves 2327
 
2.5%
sábado 2297
 
2.4%
martes 2275
 
2.4%
lunes 2274
 
2.4%
Other values (44) 33219
35.1%
2025-01-17T08:47:57.652911image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
78785
17.4%
e 65086
14.4%
d 37072
 
8.2%
1 22742
 
5.0%
2 22369
 
4.9%
o 21274
 
4.7%
r 19646
 
4.3%
0 17225
 
3.8%
s 16381
 
3.6%
i 15772
 
3.5%
Other values (25) 137051
30.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 453403
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
78785
17.4%
e 65086
14.4%
d 37072
 
8.2%
1 22742
 
5.0%
2 22369
 
4.9%
o 21274
 
4.7%
r 19646
 
4.3%
0 17225
 
3.8%
s 16381
 
3.6%
i 15772
 
3.5%
Other values (25) 137051
30.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 453403
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
78785
17.4%
e 65086
14.4%
d 37072
 
8.2%
1 22742
 
5.0%
2 22369
 
4.9%
o 21274
 
4.7%
r 19646
 
4.3%
0 17225
 
3.8%
s 16381
 
3.6%
i 15772
 
3.5%
Other values (25) 137051
30.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 453403
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
78785
17.4%
e 65086
14.4%
d 37072
 
8.2%
1 22742
 
5.0%
2 22369
 
4.9%
o 21274
 
4.7%
r 19646
 
4.3%
0 17225
 
3.8%
s 16381
 
3.6%
i 15772
 
3.5%
Other values (25) 137051
30.2%

Edad (ubicaciones)
Real number (ℝ)

High correlation 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.357365
Minimum0
Maximum110
Zeros16
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size123.2 KiB
2025-01-17T08:47:57.750106image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile30
Q150
median60
Q370
95-th percentile80
Maximum110
Range110
Interquartile range (IQR)20

Descriptive statistics

Standard deviation13.783406
Coefficient of variation (CV)0.24030751
Kurtosis0.57557376
Mean57.357365
Median Absolute Deviation (MAD)10
Skewness-0.50124579
Sum903780
Variance189.98227
MonotonicityNot monotonic
2025-01-17T08:47:57.862298image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
60 4957
31.5%
50 3701
23.5%
70 3165
20.1%
40 1622
 
10.3%
80 1286
 
8.2%
30 600
 
3.8%
20 250
 
1.6%
90 90
 
0.6%
10 68
 
0.4%
0 16
 
0.1%
ValueCountFrequency (%)
0 16
 
0.1%
10 68
 
0.4%
20 250
 
1.6%
30 600
 
3.8%
40 1622
 
10.3%
50 3701
23.5%
60 4957
31.5%
70 3165
20.1%
80 1286
 
8.2%
90 90
 
0.6%
ValueCountFrequency (%)
110 2
 
< 0.1%
90 90
 
0.6%
80 1286
 
8.2%
70 3165
20.1%
60 4957
31.5%
50 3701
23.5%
40 1622
 
10.3%
30 600
 
3.8%
20 250
 
1.6%
10 68
 
0.4%

Grupo Etario
Categorical

High correlation 

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size954.2 KiB
60-70
4842 
50-60
4145 
70-80
2736 
40-50
1897 
80-90
978 
Other values (5)
1159 

Length

Max length6
Median length5
Mean length4.999175
Min length4

Characters and Unicode

Total characters78772
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row90-110
2nd row90-110
3rd row90-110
4th row80-90
5th row80-90

Common Values

ValueCountFrequency (%)
60-70 4842
30.7%
50-60 4145
26.3%
70-80 2736
17.4%
40-50 1897
 
12.0%
80-90 978
 
6.2%
30-40 707
 
4.5%
20-30 291
 
1.8%
0-18 73
 
0.5%
90-110 60
 
0.4%
18-20 28
 
0.2%

Length

2025-01-17T08:47:58.014033image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-17T08:47:58.157539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
60-70 4842
30.7%
50-60 4145
26.3%
70-80 2736
17.4%
40-50 1897
 
12.0%
80-90 978
 
6.2%
30-40 707
 
4.5%
20-30 291
 
1.8%
0-18 73
 
0.5%
90-110 60
 
0.4%
18-20 28
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 31413
39.9%
- 15757
20.0%
6 8987
 
11.4%
7 7578
 
9.6%
5 6042
 
7.7%
8 3815
 
4.8%
4 2604
 
3.3%
9 1038
 
1.3%
3 998
 
1.3%
2 319
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 78772
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 31413
39.9%
- 15757
20.0%
6 8987
 
11.4%
7 7578
 
9.6%
5 6042
 
7.7%
8 3815
 
4.8%
4 2604
 
3.3%
9 1038
 
1.3%
3 998
 
1.3%
2 319
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 78772
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 31413
39.9%
- 15757
20.0%
6 8987
 
11.4%
7 7578
 
9.6%
5 6042
 
7.7%
8 3815
 
4.8%
4 2604
 
3.3%
9 1038
 
1.3%
3 998
 
1.3%
2 319
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 78772
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 31413
39.9%
- 15757
20.0%
6 8987
 
11.4%
7 7578
 
9.6%
5 6042
 
7.7%
8 3815
 
4.8%
4 2604
 
3.3%
9 1038
 
1.3%
3 998
 
1.3%
2 319
 
0.4%

Interactions

2025-01-17T08:47:40.830024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-17T08:47:29.565956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-17T08:47:31.179493image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-17T08:47:32.448543image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-17T08:47:34.016683image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-17T08:47:36.427617image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-17T08:47:38.064290image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-17T08:47:39.398182image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-17T08:47:40.991299image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-17T08:47:29.746679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-17T08:47:31.336651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-17T08:47:32.602785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-17T08:47:34.295805image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-17T08:47:36.723381image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-17T08:47:38.263723image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-17T08:47:39.590548image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-17T08:47:41.190400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-17T08:47:30.085258image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-17T08:47:31.511533image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-17T08:47:32.730415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-17T08:47:34.521241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-17T08:47:36.979153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-17T08:47:38.414517image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-17T08:47:39.787303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-17T08:47:41.328162image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-17T08:47:30.252595image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-17T08:47:31.672198image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-17T08:47:32.868037image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-17T08:47:34.779989image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-17T08:47:37.130539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-17T08:47:38.554484image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-17T08:47:39.949249image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-17T08:47:41.471425image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-17T08:47:30.443043image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-17T08:47:31.824041image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-17T08:47:33.001833image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-17T08:47:35.031430image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-17T08:47:37.332440image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-17T08:47:38.726478image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-17T08:47:40.106127image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-17T08:47:41.609489image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-17T08:47:30.624394image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-17T08:47:31.969909image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-17T08:47:33.175206image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-17T08:47:35.336268image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-17T08:47:37.508851image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-17T08:47:38.892592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-17T08:47:40.295973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-17T08:47:41.748585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-17T08:47:30.816759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-17T08:47:32.123494image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-17T08:47:33.440825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-17T08:47:35.858652image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-17T08:47:37.717622image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-17T08:47:39.049289image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-17T08:47:40.456428image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-17T08:47:42.166154image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-17T08:47:31.000205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-17T08:47:32.289854image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-17T08:47:33.738944image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-17T08:47:36.164422image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-17T08:47:37.906423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-17T08:47:39.244500image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-17T08:47:40.642818image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-01-17T08:47:58.372011image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ALCOHOLANEMIAANEMIA SEVERAANGINA ESTABLEATYPICAL CHEST PAINAccidente cerebrovascular hemorrágicoAccidente cerebrovascular isquémicoBloqueo auriculoventricular completoDiabetes mellitusDuracion en Terapia IntensivaDuración de estadiaEdadEdad (ubicaciones)Embolia pulmonarEndocarditis infecciosaEnfermedad arterial coronariaEnfermedad cardíaca congénitaEnfermedad renal crónicaEnfermedad valvular cardíacaEnzimas cardíacas elevadasFibrilación auricularFumadorGrupo EtarioGéneroHemoglobinaHipertensiónHipotensión ortostáticaIDInfarto de miocardio con elevación del STInfección del tracto urinarioInfección torácicaInsuficiencia cardíacaInsuficiencia cardíaca con fracción de eyección normalInsuficiencia cardíaca con fracción de eyección reducidaLesión renal agudaLeucocitosMiocardiopatíaMotivo AltaNEURO CARDIOGENIC SYNCOPENro clave admisionSHOCKShock cardiogénicoSíndrome coronario agudoSíndrome del seno enfermoTaquicardia supraventricular paroxísticaTaquicardia ventricularTipo de AdmisionTrombosis venosa profundaZona de residencia
ALCOHOL1.0000.0420.0060.0100.0000.0000.0000.0180.0290.0130.0140.0980.1010.0000.0000.0140.0000.0180.0000.0030.0000.3280.1010.1900.0810.0240.0000.0340.0360.0150.0000.0450.0290.0280.0140.0000.0000.0600.0000.0920.0150.0130.0180.0000.0100.0090.0210.0230.040
ANEMIA0.0421.0000.3020.0890.0410.0000.0070.0000.0950.1540.1540.0880.0880.0000.0270.0120.0160.2730.0050.0400.0000.0400.0900.1310.1300.0500.0120.0110.0610.0680.0150.1240.0460.1120.2690.0000.0320.0850.0120.0490.0530.0550.0340.0000.0070.0060.0820.0130.025
ANEMIA SEVERA0.0060.3021.0000.0300.0170.0000.0090.0140.0000.0340.0230.0500.0410.0020.0000.0140.0000.0850.0000.0130.0000.0020.0420.0250.0860.0000.0000.0000.0270.0000.0050.0210.0140.0090.0800.0000.0000.0220.0000.0430.0080.0090.0160.0000.0050.0000.0130.0000.000
ANGINA ESTABLE0.0100.0890.0301.0000.0470.0160.0400.0460.0000.1360.1070.0950.1050.0300.0060.1340.0210.0720.0250.1230.0570.0010.1010.0260.0000.0270.0240.0190.0980.0340.0230.1570.0990.1030.1130.0000.1030.0960.0170.0470.0540.0530.2020.0190.0210.0490.2980.0310.000
ATYPICAL CHEST PAIN0.0000.0410.0170.0471.0000.0000.0260.0240.0460.0680.0510.0850.0900.0170.0000.0880.0120.0470.0150.0650.0300.0000.0880.0300.0270.0000.0090.0000.0640.0100.0150.0890.0530.0610.0740.0000.0600.0580.0020.0470.0240.0290.1170.0080.0090.0180.0800.0150.000
Accidente cerebrovascular hemorrágico0.0000.0000.0000.0160.0001.0000.0890.0000.0000.0070.0380.0160.0180.0000.0000.0150.0000.0000.0000.0000.0000.0100.0000.0060.0000.0000.0070.0000.0110.0000.0000.0000.0000.0000.0100.0000.0000.0300.0190.0290.0000.0000.0270.0000.0000.0000.0000.0000.000
Accidente cerebrovascular isquémico0.0000.0070.0090.0400.0260.0891.0000.0160.0230.0570.0530.0370.0420.0070.0120.0530.0000.0000.0170.0000.0660.0000.0360.0040.0400.0280.0000.0360.0290.0270.0000.0000.0060.0120.0320.0000.0060.0350.0070.0700.0000.0000.0560.0000.0000.0060.0170.0090.010
Bloqueo auriculoventricular completo0.0180.0000.0140.0460.0240.0000.0161.0000.0070.1060.1070.0570.0620.0130.0000.0130.0080.0000.0070.0000.0250.0190.0650.0090.0000.0100.0000.0460.0590.0000.0120.0000.0000.0000.0280.0000.0220.0450.0000.0290.0130.0220.0010.0000.0090.0180.0650.0000.008
Diabetes mellitus0.0290.0950.0000.0000.0460.0000.0230.0071.0000.0610.0550.1580.1660.0120.0200.1050.0140.1120.0260.0260.0000.0000.1690.0100.0640.1530.0120.1130.0100.0970.0000.0390.0110.0640.1270.0000.0570.0350.0000.2300.0000.0000.0090.0000.0050.0060.0310.0300.025
Duracion en Terapia Intensiva0.0130.1540.0340.1360.0680.0070.0570.1060.0611.0000.7170.1420.1450.0420.0330.0030.0000.1300.0180.1060.0710.0130.0420.032-0.1320.0110.0210.0610.0810.0660.0340.1960.1170.1390.1800.1970.0900.0540.0100.0590.0840.0640.0640.0000.0000.1090.2280.0240.000
Duración de estadia0.0140.1540.0230.1070.0510.0380.0530.1070.0550.7171.0000.1310.1300.0470.0430.0300.0000.1260.0000.0480.0320.0240.0380.038-0.1440.0180.037-0.0280.0000.0890.0610.1380.0900.0900.1580.1640.0560.0430.000-0.0010.0760.0680.0180.0000.0000.0800.1320.0360.021
Edad0.0980.0880.0500.0950.0850.0160.0370.0570.1580.1420.1311.0000.9730.0390.1210.2470.2870.0970.0270.0720.1000.0980.6660.030-0.1880.2190.000-0.0710.0880.0740.0000.1470.0810.1140.1790.0250.0430.0580.0000.0280.0430.0360.1050.0160.0270.0230.1090.0910.050
Edad (ubicaciones)0.1010.0880.0410.1050.0900.0180.0420.0620.1660.1450.1300.9731.0000.0420.1040.2470.2050.0960.0210.0770.1010.1020.7960.038-0.1800.2190.000-0.0600.0860.0680.0000.1520.0850.1140.1840.0290.0510.0600.0000.0230.0460.0340.0980.0200.0280.0170.1110.0960.045
Embolia pulmonar0.0000.0000.0020.0300.0170.0000.0070.0130.0120.0420.0470.0390.0421.0000.0000.0980.0000.0140.0000.0180.0200.0000.0480.0050.0000.0150.0000.0000.0430.0000.0000.0470.0240.0340.0000.0000.0350.0000.0120.0570.0000.0000.0820.0000.0000.0000.0240.3310.034
Endocarditis infecciosa0.0000.0270.0000.0060.0000.0000.0120.0000.0200.0330.0430.1210.1040.0001.0000.0530.0000.0000.0090.0090.0000.0000.1170.0170.0000.0300.0000.0000.0130.0000.0000.0050.0000.0000.0000.0000.0020.0360.0000.0150.0000.0000.0300.0000.0000.0000.0120.0000.010
Enfermedad arterial coronaria0.0140.0120.0140.1340.0880.0150.0530.0130.1050.0030.0300.2470.2470.0980.0531.0000.0480.0000.0780.0710.0800.0210.2480.1070.0140.3400.0030.0270.1640.0000.0000.0130.0120.0000.0120.0000.0800.1510.0160.1160.0920.0780.1930.0030.0500.0070.0070.0920.049
Enfermedad cardíaca congénita0.0000.0160.0000.0210.0120.0000.0000.0080.0140.0000.0000.2870.2050.0000.0000.0481.0000.0190.0280.0270.0000.0010.2200.0120.0190.0390.0130.0200.0090.0120.0040.0350.0200.0190.0160.0000.0130.0230.0200.0300.0080.0090.0510.0000.0000.0000.0180.0000.024
Enfermedad renal crónica0.0180.2730.0850.0720.0470.0000.0000.0000.1120.1300.1260.0970.0960.0140.0000.0000.0191.0000.0000.0330.0240.0300.0980.0290.0820.0810.0000.0150.0590.0750.0140.1210.0530.1010.5810.0000.1120.1190.0130.0200.0870.0760.0590.0000.0100.0260.0860.0270.000
Enfermedad valvular cardíaca0.0000.0050.0000.0250.0150.0000.0170.0070.0260.0180.0000.0270.0210.0000.0090.0780.0280.0001.0000.0150.0880.0100.0190.0310.0000.0260.0000.0000.0270.0000.0000.0040.0200.0070.0110.0000.0000.0270.0210.0430.0230.0150.0410.0000.0000.0000.0200.0120.014
Enzimas cardíacas elevadas0.0030.0400.0130.1230.0650.0000.0000.0000.0260.1060.0480.0720.0770.0180.0090.0710.0270.0330.0151.0000.0000.0100.0740.0220.0230.0280.0000.0630.0830.0240.0160.1270.0820.0810.0780.0000.0000.0910.0300.0890.0550.0420.4060.0080.0190.0250.1700.0240.034
Fibrilación auricular0.0000.0000.0000.0570.0300.0000.0660.0250.0000.0710.0320.1000.1010.0200.0000.0800.0000.0240.0880.0001.0000.0180.0980.0200.0000.0000.0000.0120.0220.0280.0000.0720.0350.0570.0660.0150.0370.0640.0000.1140.0260.0130.0460.0120.0130.0000.0790.0070.034
Fumador0.3280.0400.0020.0010.0000.0100.0000.0190.0000.0130.0240.0980.1020.0000.0000.0210.0010.0300.0100.0100.0181.0000.1000.1640.0870.0560.0090.0310.0480.0000.0000.0350.0300.0100.0390.0000.0000.0390.0000.0880.0140.0180.0260.0000.0130.0240.0000.0000.022
Grupo Etario0.1010.0900.0420.1010.0880.0000.0360.0650.1690.0420.0380.6660.7960.0480.1170.2480.2200.0980.0190.0740.0980.1001.0000.0210.0670.2250.0000.0000.0930.0690.0000.1460.0780.1130.1810.0540.0470.0570.0150.0220.0430.0350.1000.0100.0310.0210.1050.0890.056
Género0.1900.1310.0250.0260.0300.0060.0040.0090.0100.0320.0380.0300.0380.0050.0170.1070.0120.0290.0310.0220.0200.1640.0211.0000.1680.0680.0040.0050.0640.0980.0000.0390.0180.0290.0440.0000.0320.0000.0100.0000.0000.0040.0790.0000.0230.0000.0140.0000.000
Hemoglobina0.0810.1300.0860.0000.0270.0000.0400.0000.064-0.132-0.144-0.188-0.1800.0000.0000.0140.0190.0820.0000.0230.0000.0870.0670.1681.0000.0410.0590.0410.0650.0220.0000.0520.0210.0440.0760.0040.0260.0130.000-0.0500.0000.0000.0350.0420.0000.0210.0140.0000.021
Hipertensión0.0240.0500.0000.0270.0000.0000.0280.0100.1530.0110.0180.2190.2190.0150.0300.3400.0390.0810.0260.0280.0000.0560.2250.0680.0411.0000.0000.0000.0690.0320.0080.0000.0000.0000.0730.0000.0590.0650.0060.0520.0380.0260.0000.0030.0180.0190.0140.0310.036
Hipotensión ortostática0.0000.0120.0000.0240.0090.0070.0000.0000.0120.0210.0370.0000.0000.0000.0000.0030.0130.0000.0000.0000.0000.0090.0000.0040.0590.0001.0000.0250.0210.0120.0070.0140.0200.0000.0000.0000.0230.0100.0340.1220.0000.0000.0300.0000.0000.0000.0370.0080.000
ID0.0340.0110.0000.0190.0000.0000.0360.0460.1130.061-0.028-0.071-0.0600.0000.0000.0270.0200.0150.0000.0630.0120.0310.0000.0050.0410.0000.0251.0000.0400.0410.0130.0190.0700.0430.0380.0160.0400.0350.0000.5810.0370.0050.1130.0000.0000.0000.0320.0000.000
Infarto de miocardio con elevación del ST0.0360.0610.0270.0980.0640.0110.0290.0590.0100.0810.0000.0880.0860.0430.0130.1640.0090.0590.0270.0830.0220.0480.0930.0640.0650.0690.0210.0401.0000.0150.0060.0250.0370.0000.0430.0000.0130.0390.0180.0530.0370.0790.5310.0000.0240.0460.1760.0370.011
Infección del tracto urinario0.0150.0680.0000.0340.0100.0000.0270.0000.0970.0660.0890.0740.0680.0000.0000.0000.0120.0750.0000.0240.0280.0000.0690.0980.0220.0320.0120.0410.0151.0000.0000.0000.0170.0000.0910.0000.0060.0470.0000.1000.0000.0090.0430.0000.0050.0000.0270.0020.019
Infección torácica0.0000.0150.0050.0230.0150.0000.0000.0120.0000.0340.0610.0000.0000.0000.0000.0000.0040.0140.0000.0160.0000.0000.0000.0000.0000.0080.0070.0130.0060.0001.0000.0350.0300.0120.0040.0000.0060.0110.0080.0360.0000.0320.0420.0000.0000.0000.0310.0000.000
Insuficiencia cardíaca0.0450.1240.0210.1570.0890.0000.0000.0000.0390.1960.1380.1470.1520.0470.0050.0130.0350.1210.0040.1270.0720.0350.1460.0390.0520.0000.0140.0190.0250.0000.0351.0000.6220.6650.2050.0000.3120.1870.0370.0800.0890.0900.0350.0360.0190.0600.1580.0400.000
Insuficiencia cardíaca con fracción de eyección normal0.0290.0460.0140.0990.0530.0000.0060.0000.0110.1170.0900.0810.0850.0240.0000.0120.0200.0530.0200.0820.0350.0300.0780.0180.0210.0000.0200.0700.0370.0170.0300.6221.0000.1650.0720.0000.1740.0440.0080.1690.0270.0000.0200.0160.0010.0260.0980.0180.000
Insuficiencia cardíaca con fracción de eyección reducida0.0280.1120.0090.1030.0610.0000.0120.0000.0640.1390.0900.1140.1140.0340.0000.0000.0190.1010.0070.0810.0570.0100.1130.0290.0440.0000.0000.0430.0000.0000.0120.6650.1651.0000.1890.0000.2280.2320.0350.1440.1410.1160.0230.0270.0160.0490.1050.0310.000
Lesión renal aguda0.0140.2690.0800.1130.0740.0100.0320.0280.1270.1800.1580.1790.1840.0000.0000.0120.0160.5810.0110.0780.0660.0390.1810.0440.0760.0730.0000.0380.0430.0910.0040.2050.0720.1891.0000.0000.2030.2090.0100.0160.1470.1290.0230.0000.0120.0600.1410.0280.000
Leucocitos0.0000.0000.0000.0000.0000.0000.0000.0000.0000.1970.1640.0250.0290.0000.0000.0000.0000.0000.0000.0000.0150.0000.0540.0000.0040.0000.0000.0160.0000.0000.0000.0000.0000.0000.0001.0000.0000.0300.000-0.0320.0160.0000.0000.0000.0000.0000.0000.0000.000
Miocardiopatía0.0000.0320.0000.1030.0600.0000.0060.0220.0570.0900.0560.0430.0510.0350.0020.0800.0130.1120.0000.0000.0370.0000.0470.0320.0260.0590.0230.0400.0130.0060.0060.3120.1740.2280.2030.0001.0000.1650.0000.0850.0950.0990.0400.0270.0160.0990.0870.0370.022
Motivo Alta0.0600.0850.0220.0960.0580.0300.0350.0450.0350.0540.0430.0580.0600.0000.0360.1510.0230.1190.0270.0910.0640.0390.0570.0000.0130.0650.0100.0350.0390.0470.0110.1870.0440.2320.2090.0300.1651.0000.0270.0630.5590.4020.0760.0150.0240.1350.1860.0130.004
NEURO CARDIOGENIC SYNCOPE0.0000.0120.0000.0170.0020.0190.0070.0000.0000.0100.0000.0000.0000.0120.0000.0160.0200.0130.0210.0300.0000.0000.0150.0100.0000.0060.0340.0000.0180.0000.0080.0370.0080.0350.0100.0000.0000.0271.0000.0440.0090.0060.0470.0000.0270.0000.0070.0000.000
Nro clave admision0.0920.0490.0430.0470.0470.0290.0700.0290.2300.059-0.0010.0280.0230.0570.0150.1160.0300.0200.0430.0890.1140.0880.0220.000-0.0500.0520.1220.5810.0530.1000.0360.0800.1690.1440.016-0.0320.0850.0630.0441.0000.0450.0880.0690.0360.0230.0220.1180.0400.078
SHOCK0.0150.0530.0080.0540.0240.0000.0000.0130.0000.0840.0760.0430.0460.0000.0000.0920.0080.0870.0230.0550.0260.0140.0430.0000.0000.0380.0000.0370.0370.0000.0000.0890.0270.1410.1470.0160.0950.5590.0090.0451.0000.6000.0250.0000.0070.0880.1000.0000.000
Shock cardiogénico0.0130.0550.0090.0530.0290.0000.0000.0220.0000.0640.0680.0360.0340.0000.0000.0780.0090.0760.0150.0420.0130.0180.0350.0040.0000.0260.0000.0050.0790.0090.0320.0900.0000.1160.1290.0000.0990.4020.0060.0880.6001.0000.0610.0000.0120.0980.0770.0140.000
Síndrome coronario agudo0.0180.0340.0160.2020.1170.0270.0560.0010.0090.0640.0180.1050.0980.0820.0300.1930.0510.0590.0410.4060.0460.0260.1000.0790.0350.0000.0300.1130.5310.0430.0420.0350.0200.0230.0230.0000.0400.0760.0470.0690.0250.0611.0000.0300.0480.0480.1810.0690.020
Síndrome del seno enfermo0.0000.0000.0000.0190.0080.0000.0000.0000.0000.0000.0000.0160.0200.0000.0000.0030.0000.0000.0000.0080.0120.0000.0100.0000.0420.0030.0000.0000.0000.0000.0000.0360.0160.0270.0000.0000.0270.0150.0000.0360.0000.0000.0301.0000.0230.0000.0000.0000.000
Taquicardia supraventricular paroxística0.0100.0070.0050.0210.0090.0000.0000.0090.0050.0000.0000.0270.0280.0000.0000.0500.0000.0100.0000.0190.0130.0130.0310.0230.0000.0180.0000.0000.0240.0050.0000.0190.0010.0160.0120.0000.0160.0240.0270.0230.0070.0120.0480.0231.0000.0000.0240.0000.000
Taquicardia ventricular0.0090.0060.0000.0490.0180.0000.0060.0180.0060.1090.0800.0230.0170.0000.0000.0070.0000.0260.0000.0250.0000.0240.0210.0000.0210.0190.0000.0000.0460.0000.0000.0600.0260.0490.0600.0000.0990.1350.0000.0220.0880.0980.0480.0000.0001.0000.0690.0000.000
Tipo de Admision0.0210.0820.0130.2980.0800.0000.0170.0650.0310.2280.1320.1090.1110.0240.0120.0070.0180.0860.0200.1700.0790.0000.1050.0140.0140.0140.0370.0320.1760.0270.0310.1580.0980.1050.1410.0000.0870.1860.0070.1180.1000.0770.1810.0000.0240.0691.0000.0100.018
Trombosis venosa profunda0.0230.0130.0000.0310.0150.0000.0090.0000.0300.0240.0360.0910.0960.3310.0000.0920.0000.0270.0120.0240.0070.0000.0890.0000.0000.0310.0080.0000.0370.0020.0000.0400.0180.0310.0280.0000.0370.0130.0000.0400.0000.0140.0690.0000.0000.0000.0101.0000.010
Zona de residencia0.0400.0250.0000.0000.0000.0000.0100.0080.0250.0000.0210.0500.0450.0340.0100.0490.0240.0000.0140.0340.0340.0220.0560.0000.0210.0360.0000.0000.0110.0190.0000.0000.0000.0000.0000.0000.0220.0040.0000.0780.0000.0000.0200.0000.0000.0000.0180.0101.000

Missing values

2025-01-17T08:47:42.540323image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-01-17T08:47:43.119890image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-01-17T08:47:43.646909image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Nro clave admisionIDEdadGéneroZona de residenciaTipo de AdmisionDuración de estadiaDuracion en Terapia IntensivaMotivo AltaFumadorALCOHOLDiabetes mellitusHipertensiónEnfermedad arterial coronariaMiocardiopatíaEnfermedad renal crónicaHemoglobinaLeucocitosPlaquetasGLUCOSAUREACreatininaPéptido natriurético tipo BEnzimas cardíacas elevadasFracción de eyecciónANEMIA SEVERAANEMIAANGINA ESTABLESíndrome coronario agudoInfarto de miocardio con elevación del STATYPICAL CHEST PAINInsuficiencia cardíacaInsuficiencia cardíaca con fracción de eyección reducidaInsuficiencia cardíaca con fracción de eyección normalEnfermedad valvular cardíacaBloqueo auriculoventricular completoSíndrome del seno enfermoLesión renal agudaAccidente cerebrovascular isquémicoAccidente cerebrovascular hemorrágicoFibrilación auricularTaquicardia ventricularTaquicardia supraventricular paroxísticaEnfermedad cardíaca congénitaInfección del tracto urinarioNEURO CARDIOGENIC SYNCOPEHipotensión ortostáticaEndocarditis infecciosaTrombosis venosa profundaShock cardiogénicoSHOCKEmbolia pulmonarInfección torácicaFecha de AdmisionFecha de AltaEdad (ubicaciones)Grupo Etario
08656509172.099FemeninoRuralEmergencia11DAMA000110059.046.064102370.6NaN0NaN1100000000000000000000000000.02018-05-01miércoles, 30 de mayo de 20189090-110
112766633310.092MasculinoUrbanaEmergencia11DAMA0000000141.0154.0163116281NaN0NaN0000000000010000000000000000.02018-12-01viernes, 7 de diciembre de 20189090-110
2453246963.098MasculinoRuralEmergencia62DAMA0000000136.0119.0121191751.3EMPTY0350000000000000000000000000000.02017-11-01sábado, 25 de noviembre de 20179090-110
34372387289.087MasculinoRuralAmbulatoria125DAMA0011110107.0102.0158201530.8EMPTY0240000001100000000000000000000.02017-11-01viernes, 24 de noviembre de 20178080-90
44060380825.084MasculinoUrbanaEmergencia71DAMA000111097.091.0183140862.03EMPTY0220100001100001000000000000000.02017-11-01miércoles, 8 de noviembre de 20178080-90
54078380927.085FemeninoUrbanaEmergencia88DAMA001110095.089.0839232180.44EMPTY1350100001100000000000000000000.02017-11-01jueves, 9 de noviembre de 20178080-90
64480389544.088FemeninoUrbanaEmergencia55DAMA0001000111.0111.0152140721.3EMPTY1420001000001000100000000000000.02017-11-01martes, 21 de noviembre de 20178080-90
710601577201.082FemeninoUrbanaEmergencia33DAMA0000100122.0121.0111NaN691.9NaN1250001100000001000000000000000.02018-09-01domingo, 9 de septiembre de 20188080-90
813949658656.086MasculinoUrbanaEmergencia44DAMA0011100104.054.0171NaN280.9NaN0460000000000000000000000000000.02019-01-01sábado, 26 de enero de 20198080-90
9704263000.085FemeninoRuralAmbulatoria33DAMA0011010109.0106.0300NaN310.9NaN0300001100000000000000000000000.02017-05-01sábado, 13 de mayo de 20178080-90
Nro clave admisionIDEdadGéneroZona de residenciaTipo de AdmisionDuración de estadiaDuracion en Terapia IntensivaMotivo AltaFumadorALCOHOLDiabetes mellitusHipertensiónEnfermedad arterial coronariaMiocardiopatíaEnfermedad renal crónicaHemoglobinaLeucocitosPlaquetasGLUCOSAUREACreatininaPéptido natriurético tipo BEnzimas cardíacas elevadasFracción de eyecciónANEMIA SEVERAANEMIAANGINA ESTABLESíndrome coronario agudoInfarto de miocardio con elevación del STATYPICAL CHEST PAINInsuficiencia cardíacaInsuficiencia cardíaca con fracción de eyección reducidaInsuficiencia cardíaca con fracción de eyección normalEnfermedad valvular cardíacaBloqueo auriculoventricular completoSíndrome del seno enfermoLesión renal agudaAccidente cerebrovascular isquémicoAccidente cerebrovascular hemorrágicoFibrilación auricularTaquicardia ventricularTaquicardia supraventricular paroxísticaEnfermedad cardíaca congénitaInfección del tracto urinarioNEURO CARDIOGENIC SYNCOPEHipotensión ortostáticaEndocarditis infecciosaTrombosis venosa profundaShock cardiogénicoSHOCKEmbolia pulmonarInfección torácicaFecha de AdmisionFecha de AltaEdad (ubicaciones)Grupo Etario
157471902304794.026MasculinoRuralEmergencia2317EXPIRY0000000NaNNaNNaNNaNNaNNaNNaN0600000001010000000000000000100.02017-07-01miércoles, 2 de agosto de 20172020-30
157489404533704.030FemeninoRuralEmergencia11EXPIRY0000000NaNNaNNaNNaNNaNNaNNaN0600000000000000000000000000100.02018-07-01viernes, 6 de julio de 20183020-30
15749231378564.021FemeninoUrbanaEmergencia10EXPIRY0000100NaNNaNNaNNaNNaNNaNNaN0600000001010000000000000001100.02017-04-01jueves, 20 de abril de 20172020-30
157509924585992.020MasculinoRuralEmergencia10EXPIRY0000000NaNNaNNaNNaNNaNNaNNaN0600000000000000000000000000000.02018-08-01viernes, 3 de agosto de 20182018-20
15751147385830.020MasculinoUrbanaEmergencia22EXPIRY0000010149.0233.012260772.5EMPTY0250000000000001000000000001100.02017-04-01martes, 11 de abril de 20172018-20
15752683261600.020FemeninoUrbanaEmergencia22EXPIRY000000175.0228.03911081327.3NaN0NaN0101100000101000000000001100.02017-05-01jueves, 11 de mayo de 20172018-20
157536384432128.018MasculinoUrbanaEmergencia33EXPIRY000001071.0184.063NaN951.7NaN0300111001101001000000000000000.02018-02-01domingo, 4 de febrero de 2018100-18
157545435145216.018FemeninoUrbanaEmergencia55EXPIRY0000100149.0128.015193380.59NaN0480000001010100001000000001100.02017-12-01sábado, 30 de diciembre de 2017100-18
157553279356253.013MasculinoRuralEmergencia55EXPIRY0000000138.089.024980160.8NaN0400000000000000000000000000100.02017-09-01viernes, 29 de septiembre de 2017100-18
157563510362015.016MasculinoUrbanaEmergencia10EXPIRY0000000NaNNaNNaN105NaNNaNNaN0600000000000000000000000001110.02017-10-01jueves, 5 de octubre de 2017100-18